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By Tianyi Wu


Promotion is one of the most important elements in marketing. It is often desirable to find merit in an object (e.g., product, person, organization, or other business entity) and promote it in an appropriate community confidently. In this thesis, we motivate and discuss a novel class of data mining problems, called promotion analysis, for promoting a given object in a multi-dimensional space by leveraging object ranking information. The key observation is that most objects may not be highly ranked in the global space, where all objects are compared by all aspects; in contrast, there often exist interesting and meaningful local spaces in which the given object becomes prominent. Therefore, our general goal is to break down the data space and discover the most interesting local spaces in an effective and efficient way. We formally present the promotion analysis problem and formulate its variants and related notions. The promotion analysis problem is highly practical and useful in a wide spectrum of decision support applications. Typical application examples include merit discovery, product positioning and customer targeting, object profiling and summarization, identification of interesting features, and explorative search of objects. In fact, these applications are not new as they have bee

Year: 2010
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